Search Results (5,106)

Metabolic heterogeneity within the cell microenvironment is a key driver of cancer progression and resistance to therapy. However, current approaches lack the spatial and temporal resolution required to capture its dynamics in living systems. While recent advances in 3D cell culture models and metabolomic profiling have improved our understanding of the tumor niche, their integration with real-time optical sensing remains underdeveloped. Here, we present an integrated platform combining a 3D-printed microreactor culture chamber with Raman spectroscopy to enable non-invasive, spatially resolved metabolic monitoring of living cell cultures. Our microreactor platform generates controlled oxygen and nutrient cues while simultaneously acquiring label-free Raman spectra, revealing extracellular metabolic fingerprints linked to cell catabolism (e.g., glucose and lactate shifts) and acidification. Analysis across four cell lines uncovered temporal evolution as the dominant source of metabolic variance, while spatial heterogeneity along oxygen gradients is a secondary factor. In particular, diffusion-limited regions exhibited localized acidification and accumulation of stress biomarkers—such as the release of nucleotides—features that cannot be detected using conventional bulk assays. By providing a versatile platform for real-time mapping, this work enables the mechanistic dissection of cell adaptation to microenvironmental stress and supports the prediction of metabolic signatures underlying drug response and treatment outcomes. Full article

Why should a genome be protected? Because it contains our most private data! A genome contains an organism’s set of genetic material (DNA and, in viruses, RNA), and it contains all genes and non-coding sequences. The structure of DNA was described by Watson and Crick in 1953, but the first studies were conducted a century earlier by Miescher, who described the structure and chemical composition of the nucleus. The first action aimed at securing the results of genetic research was the creation of databases for the results obtained using genetic fingerprinting technology. The discovery of the sequencing method and the introduction of the polymerase chain reaction laid the foundations for understanding the genome’s function. Automated DNA sequencing proved to be hundreds of times faster than traditional methods, thus reducing the cost and time of genome analyses. Thousands of genomic data points are stored in private and governmental databases. The security of patients’ genomic data must be ensured by protecting it from unauthorized use while, at the same time, enabling research for the sake of public health. The falling prices of genome sequencing and the increasing availability of commercial sequencing for the public could result in ethical problems and undermine the safety of personal information. Full article

Wildfires that destroy residential infrastructure can generate chemically complex soil contamination; however, post-fire screening is often limited and does not directly reflect biological hazards. Herein, we integrated a multi-stress lux-based whole-cell bioreporter panel of genetically engineered Escherichia coli strains with non-targeted LC-MS profiling to obtain a mechanism-informed assessment of soils collected from a residential property impacted by the January 2025 Los Angeles wildfires. The bioreporter panel resolved heterogeneous and statistically significant stress signatures across the analyzed samples. In particular, extracts from U3–U5 produced selective suppression of the membrane and fatty acid biosynthesis bioreporters, along with reduced growth. In contrast, extract U5 induced a proteotoxic heat-shock response signature. In parallel, non-targeted LC-MS detected 1813 chemical features and enabled the putative annotation of a subset of signals consistent with combustion-derived organics and reactive electrophiles, providing a chemical context for the observed bioassay fingerprints. The integrated workflow provides mechanism-resolved hazard triage within 48 h, as implemented herein (24 h elutriate preparation plus up to 20 h microplate kinetics), supporting the prioritization of hotspots for confirmatory analysis, remediation, and risk assessment. Full article

The application of machine learning (ML) models to accelerate the discovery of high-transfection-potency ionizable lipids has gained significant momentum in advancing lipid nanoparticle (LNP)-mediated RNA delivery. In the present study, we adopt a screening-oriented evaluation framework based on early-recognition ranking metrics tailored to high-throughput discovery. Model performance was assessed using the enrichment factor (EF), normalized discounted cumulative gain (NDCG), and HitRate at the top 10% of the ranked list, with uncertainty quantified via 1000 nonparametric bootstrap resamples. To assess robustness of conclusions, additional analyses were conducted at the top 1% and top 5% thresholds, reflecting increasingly stringent prioritization scenarios. Four predictive models—XGBoost, Random Forest, Elastic Net, and Quantile Regression Forest—were evaluated across three molecular feature representations, circular Morgan fingerprints, expert-crafted descriptors, and Grover graph embeddings, using a held-out test set. Across all models and thresholds, Morgan fingerprints consistently yielded superior early-recognition performance. The best-performing configuration—XGBoost with Morgan fingerprints—achieved EF@10% = 4.850 (95% CI [3.182, 6.818]), NDCG@10% = 0.628 (95% CI [0.234, 0.909]), and HitRate@10% = 0.493 (95% CI [0.318, 0.683]), corresponding to nearly fivefold enrichment over random selection and identification of highly potent lipids in approximately half of the prioritized candidates. Threshold-sensitivity analyses revealed that although stricter cutoffs (top 1% and top 5%) exhibit greater variability, the relative performance ordering of molecular representations remains stable. Bootstrap distributional comparisons further demonstrate that Morgan fingerprints provide not only higher but also more consistent screening performance than expert descriptors and Grover embeddings. Collectively, these results indicate that molecular representation—rather than model architecture—is the primary determinant of early-recognition performance in ionizable lipid discovery and that this conclusion is robust across multiple screening depths. Full article

Biometric sensing systems enable accurate identity recognition using unique physiological traits. These systems can be unimodal (single trait) or multimodal (multiple traits, such as iris and fingerprint). Biometric templates, digital representations of these traits, enhance security over traditional methods but are vulnerable to attacks. Unlike passwords, compromised templates cannot be replaced, necessitating robust protection. Various security schemes exist, including cancellable biometrics, biometric cryptosystems, sensing technology, and biometrics in the encrypted domain. Cancellable biometrics apply transformations, such as biometric salting, to obscure the original data. Biometric cryptosystems integrate cryptographic techniques, including key generation and key binding, to enhance security. Biometrics in the encrypted domain, such as homomorphic encryption, ensures data remains encrypted during storage and computation. This survey focuses on the fuzzy vault method, a key-binding biometric cryptosystem. It analyses its applications, security performance, and associated challenges across different domains. By analysing advancements in fuzzy vault mechanisms, this paper provides insights into enhancing sensor-based biometric security. The study aims to serve as a reference for researchers exploring secure and efficient biometric authentication methods, ensuring robust protection against unauthorised access while maintaining the integrity and usability of biometric data in real-world applications. Full article

Objectives: Concentrated pills, as a modernization and upgrade of traditional pills, have achieved significant advancements in dosage form. However, their extended disintegration and dispersion times have become a major limitation to their therapeutic efficacy. Therefore, an in-depth study and explanation of the dissolution mechanism of concentrated pills, along with the development of processing technology to control dissolution time, has emerged as a critical bottleneck in improving the quality of concentrated pills. Methods: In this study, the Liuwei Dihuang (LWDH) concentrated pill, derived from the classical Liuwei Dihuang pill, was selected as a representative model. Two drying methods—hot-air drying and hot air–microwave combined drying—were comparatively investigated to evaluate their effects on dissolution time. The dissolution behavior was elucidated by analyzing water migration during the dissolution process, as well as the textural properties and internal structural characteristics of the pills using Low-Field Nuclear Magnetic Resonance (LF-NMR), Micro-Computed Tomography (Micro-CT), texture analysis, and other modern techniques. Results: The results indicated that: (i) The rate of water absorption during the dissolution process of the LWDH pill was influenced by the number and size of the internal pores. (ii) Hot air–microwave combined drying results in more pores and faster dissolution. (iii) High-Performance Liquid Chromatography (HPLC) fingerprints showed no significant differences in the active ingredients between the samples. Conclusions: The drying method significantly affected the internal structure of the pills, suggesting that controlling the drying process could address the prolonged dissolution time of concentrated pills. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Background/Objectives: Selective inhibition of JAK1 remains a major challenge in cytokine-signaling therapeutics due to the high structural similarity of the JAK family. Here, we present an integrated computational framework that combines large-scale binding-site conformational analysis, ensemble docking, and protein–ligand interaction fingerprinting (PLIF) to elucidate the structural determinants of JAK1 selectivity and prioritize JAK1-biased scaffolds. Methods: A curated set of JAK1 and JAK2 catalytic-domain structures was clustered to capture binding-site diversity, and representative conformers were evaluated using >2300 annotated ligands. Docking performance was assessed via AUC, early enrichment metrics, and structural pose validation against experimentally resolved complexes. The workflow was subsequently applied to a library of ~6000 drug-like compounds to prioritize candidates with predicted JAK1 preference. Results: Across the ensemble, the most predictive features reliably separated active from inactive ligands (AUC = 0.78–0.82) and captured subtle, systematic rank shifts supporting the reported JAK1 bias. Interaction fingerprint analysis revealed a conserved hinge-binding motif required for potency, alongside a JAK1-enriched hotspot adjacent to Glu aD.55 that contributes to isoform discrimination. Applied to a library of ~6000 drug-like molecules, the workflow yielded 174 candidates predicted to exhibit preferential JAK1 recognition and reduced JAK2 engagement. Conclusions: These findings define the structural and physicochemical features underlying JAK1 selectivity and illustrate how ensemble-based modeling can guide the discovery of next-generation selective kinase inhibitors. Full article

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments. Full article

Background: Obesity is a heterogeneous chronic disease in which eating behavior phenotypes may influence treatment response. Yet, anti-obesity medication (AOM) selection is still largely guided by anthropometric and metabolic parameters, with limited use of behavioral phenotyping in routine practice. We evaluated whether multidimensional eating behavior changes, measured by the Brazilian Eating Behavior Phenotype Scale (Escala de Fenótipos do Comportamento Alimentar, EFCA), differ across commonly used AOMs in a real-world cohort. Methods: We conducted a retrospective, observational, real-world study in obesity outpatient care settings in São Paulo, Brazil. Adults with obesity (18–65 years) treated with a single principal AOM for 6 months and paired baseline/6-month follow-up EFCA and anthropometric data were included. Analyses focused on early responders (≥5% total body weight loss at 3 months). Five AOM groups available in Brazil were analyzed: semaglutide (oral or subcutaneous), naltrexone/bupropion, sibutramine, topiramate, and tirzepatide. Outcomes included percent weight loss, EFCA total score, and five EFCA subscales (hedonic, emotional, compulsive, hyperphagic, disorganized). Within-medication behavioral changes were assessed using paired tests and standardized effect sizes (Cohen’s dz, 95% CI), summarized in heatmap form. Results: The analytical cohort comprised 66 early responders with paired EFCA assessments at baseline and 6 months. EFCA profiling revealed distinct behavioral response fingerprints across AOMs. Effect size mapping showed predominantly large behavioral effects (many dz ≥ 0.8) in hedonic, emotional, hyperphagic, and compulsive domains. Strongest signals included emotional eating reductions with naltrexone/bupropion (dz 2.04), tirzepatide (dz 1.77), semaglutide (dz 1.52), and topiramate (dz 1.54); hedonic reductions with tirzepatide (dz 2.06), semaglutide (dz 1.55), and naltrexone/bupropion (dz 1.52); hyperphagic reductions with tirzepatide (dz 1.50) and semaglutide (dz 1.34); and compulsive reductions with topiramate (dz 1.41) and consistent effects across tirzepatide, semaglutide, and sibutramine (≈dz 0.95–0.96). Disorganized eating showed heterogeneous/attenuated responsiveness, from near-null with tirzepatide (dz 0.03) to large but imprecise effects in smaller groups (e.g., topiramate dz 1.24, wide CI). Conclusions: In this responder-enriched real-world cohort, AOMs showed distinct and reproducible EFCA behavioral signatures, supporting a clinically actionable phenotype-informed framework to prioritize, sequence, and monitor obesity pharmacotherapy beyond nonspecific weight reduction, while highlighting disorganization as a potential target for adjunctive behavioral strategies. Full article

Background/Objectives: Coffea canephora (robusta) and Coffea liberica var. dewevrei (excelsa) cultivated in the Comoros islands represent understudied coffee varieties grown in a unique volcanic terroir. Despite their agricultural significance and potential bioactive value, no comprehensive biochemical or nutritional characterization of these Comorian coffees had previously been conducted. This study therefore aimed to provide the first integrated biochemical and nutritional characterization of both varieties and to evaluate the influence of the islands’ specific edaphoclimatic conditions on their chemical composition. Methods: An integrated analytical approach was employed, combining UV-Vis spectrophotometry, HPLC, ionomics, and FTIR-ATR spectroscopy to quantify polyphenols, flavonoids, caffeine, soluble sugars, antioxidant activity, mineral profiles, and macromolecular composition of green coffee beans from both species. Results: Robusta exhibited significantly higher levels of total polyphenols (121.79 ± 2.73 mg GAE/g), total flavonoids (29.43 ± 2.20 mg QE/g), caffeine (1.52% w/w), total soluble sugars (60.47 ± 3.37 mg GE/g), and antioxidant activity (64.97 ± 6.25 mM Trolox eq/g). Conversely, excelsa demonstrated a distinct mineral profile, with significantly higher concentrations of magnesium, calcium, sodium, zinc, and copper. FTIR spectroscopy confirmed distinct vibrational fingerprints between the two species, particularly in lipid and carbohydrate signatures. Conclusions: These findings position Comorian robusta as a potent source of antioxidants and stimulants, while excelsa offers a nutritionally balanced profile with nutraceutical potential, providing a scientific basis for valorizing both varieties as high-value niche products and contributing to the preservation of coffee agro-biodiversity. Full article

Actinidia eriantha is an endemic kiwifruit species in China with high nutritional value and breeding potential. As a typical dioecious fruit tree, most currently bred cultivars are female, while the development of male pollinizer cultivars remains insufficiently studied and reported. Through long-term collection and evaluation of wild germplasm resources, our research team bred a male cultivar ‘Ganxiong 1’ with both ornamental and pollination value. In this study, the phenological traits, floral characteristics, major biological traits, ploidy levels, and genetic diversity of ‘Ganxiong 1’ were systematically analyzed and compared with those of the commonly used pollinizer ‘Moshan 4’. The results showed that ‘Ganxiong 1’ exhibited stable genetic traits, with branch bleeding occurring in late February and flowering in early May, highly overlapping with the flowering period of most female A. eriantha cultivars. It produced bright red flowers arranged in false dichasial cymes, showing high ornamental value. The average number of anthers per flower was 140.24, and the number of pollen grains per anther reached 8.57 × 10⁴, with a pollen viability of 97.64% and a pollen tube length of 127.25 μm, indicating strong pollination potential. Ploidy and SSR analyses revealed that ‘Ganxiong 1’ is a diploid cultivar and is genetically distinct from previously reported A. eriantha cultivars at the DNA level. Regarding pollination effects, the fruit set rate, single fruit weight, seed number, SSC, and AsA content of ‘Ganlv 1’ fruits pollinated with ‘Ganxiong 1’ were significantly higher than those pollinated with ‘Moshan 4’, while the TA content was significantly lower than that of ‘Moshan 4’ pollination. In conclusion, ‘Ganxiong 1’ exhibits high stability and distinctiveness in phenological, morphological, cytological, and genetic characteristics. It can be considered a new ornamental and pollination dual-purpose cultivar of A. eriantha and provides an important parental resource for kiwifruit breeding programs. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

Background/Objectives: Cyclooxygenase-2 (COX-2) is a well-established target in the development of anti-inflammatory drugs due to its central role in mediating inflammation. The identification of novel COX-2 inhibitors remains a key focus in pharmaceutical research. This study aimed to develop a robust and interpretable machine learning framework to predict COX-2 inhibitory activity and support virtual screening efforts. Methods: A curated dataset of 2052 compounds was obtained from the ChEMBL database. Molecular structures were encoded using Morgan fingerprints derived from SMILES representations. Several machine learning algorithms were trained and evaluated, including ensemble-based methods. Model performance was assessed using internal validation and external test sets. Robustness was further evaluated through Y-randomization tests. Model interpretability was investigated using SHAP (SHapley Additive exPlanations) analysis to identify key structural features contributing to activity. Results: Among the evaluated models, ensemble methods demonstrated superior predictive performance, with the Random Forest algorithm providing the most consistent and reliable results across validation and external datasets. Y-randomization confirmed that the model predictions were not due to chance correlations. SHAP analysis revealed that the most influential features corresponded to chemically meaningful substructures aligned with known COX-2 pharmacophore characteristics. The final optimized model was successfully deployed as a publicly accessible web application for real-time prediction using SMILES input. Conclusions: This study demonstrates the effectiveness of explainable machine learning approaches in predicting COX-2 inhibitory activity. The developed framework provides a reliable and interpretable tool for accelerating COX-2 inhibitor discovery and facilitating virtual screening in drug development. Full article

Reliable identification of deceased individuals may be difficult when conventional biometric methods such as facial recognition, fingerprint analysis, or DNA profiling cannot be applied. In such cases, medical imaging records acquired during a person’s lifetime may serve as an alternative source of identifying information. Certain anatomical structures visible in computed tomography (CT), including the sphenoid sinus, exhibit considerable inter-individual variability while remaining relatively stable within the same individual. This study investigates the feasibility of using sphenoid sinus morphology as an anatomical biometric for automated identification from head CT scans. Identification is formulated as a ranking problem in which a query CT examination is compared with a reference database using geometric descriptors derived from segmentation masks, reducing dependence on CT intensity values. The dataset consisted of CT scans from 816 individuals acquired in two patient positioning modes: Head First Supine (HFS) and Head First Prone (HFP). Several deep learning architectures, including YOLOv8 variants, YOLO11L-seg, UNet++, DeepLabV3+, HRNet, and SegFormer-B2, were evaluated for sphenoid sinus segmentation. Based on F1-score performance and cross-mode stability, YOLO11L-seg was selected and further trained to construct a database of binary masks representing individual sphenoid sinus anatomy. Identification was performed using pairwise mask comparison based on the Intersection over Union (IoU) metric. To reduce the influence of segmentation artifacts and slice-level variability, the final similarity score for each candidate was computed as the average of the four highest IoU values across slice comparisons. Individuals were ranked according to similarity, and identification was considered successful if the correct subject appeared among the top five candidates and exceeded a predefined similarity threshold. The proposed approach achieved Top-5 identification accuracies of 97.27% for HFP and 87.67% for HFS acquisitions. These results demonstrate the feasibility of using sphenoid sinus geometry as a stable anatomical biometric for automated identification. The key contribution of this study is the introduction of a ranking-based identification framework that utilizes anatomical biometrics derived from CT data for reliable patient matching. Full article